K-RAS Associated Gene-Mutation-Based Algorithm for Prediction of Treatment Response of Patients with Subtypes of Breast Cancer and Especially Triple-Negative Cancer.

ABSTRACT:

Purpose

There is an urgent need for developing new biomarker tools to accurately predict treatment response of breast cancer, especially the deadly triple-negative breast cancer. We aimed to develop gene-mutation-based machine learning (ML) algorithms as biomarker classifiers to predict treatment response of first-line chemotherapy with high precision.

Methods

Random Forest ML was applied to screen the algorithms of various combinations of gene mutation profiles of primary tumors at diagnosis using a TCGA Cohort (n = 399) with up to 150 months follow-up as a training set and validated in a MSK Cohort (n = 807) with up to 220 months follow-up. Subtypes of breast cancer including triple-negative and luminal A (ER+, PR+ and HER2-) were also assessed. The predictiv

SUBMITTER: Johnson H 

PROVIDER: S-EPMC9657686 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature